1. Introduction

Hip joint replacement (HJR) has firmly established itself as one of the most successful methods of treatment of severe degenerative (osteoarthritis, aseptic necrosis of the femoral head, etc.) and inflammatory (rheumatoid arthritis, etc.) diseases of the hip joints. Hundreds of thousands of such operations are performed annually in the world, and a significant increase in their number is predicted by 2030-2050 [1,2,3]. The high efficacy of total joint replacement is supported by registry data: the survival rate of modern implants exceeds 95% within 10 years after surgery. This means that the vast majority of patients experience long-term improvements in joint function and quality of life. However, an increase in the number of surgeries is also associated with an absolute increase in the number of complications, which, although relatively rare, can undermine the success of treatment [3,6].

According to statistics, up to 3% of patients experience serious complications in the first 30 days after arthroplasty, requiring revision surgery. Among the early failures, infectious complications, such as periprosthetic infections, are the most challenging to treat. Other significant issues include endoprosthesis dislocations, perioperative thromboembolic events, and other risks. For example, without prophylaxis, deep vein thrombosis occurs in 45–57% of patients after total hip arthroplasty, and the incidence of pulmonary embolism reaches 0.3–6%, with up to a quarter of cases of pulmonary embolism being fatal [4,7]. These data highlight the need to optimize approaches to perioperative management of patients.

The modern paradigm of optimizing the results of major joint arthroplasty is based on the principles of personalized medicine. This means taking into account individual predictors – risk factors and prognostic criteria that influence the outcome of surgery – using methods of mathematical prediction of outcomes. Identifying factors in a particular patient associated with an increased risk of complications or unsatisfactory outcome allows for pre-emptive correction of treatment and rehabilitation tactics [5,8].

The use of predictive mathematical models and algorithms based on large clinical data sets opens up opportunities for an informed choice of the optimal prosthesis, fixation method, preventive measures, and other aspects of treatment. This review article presents the achievements of global science, the Commonwealth of Independent States (CIS) countries, and Uzbekistan in improving the effectiveness of hip arthroplasty using predictors and mathematical forecasting methods.

2. Global Experience and Modern Approaches

Total hip arthroplasty (THA) has a more than half-century history of development, during which outstanding results have been achieved. Nevertheless, even in the leading clinics of the world, the search for ways to improve outcomes and reduce the frequency of complications continues. A key area is the systematic study of outcome predictors – factors that allow to predict both the success of surgery and the likelihood of complications [9,10].

These predictors can be related to the patient, the underlying disease, the type of surgery, and the postoperative care. Based on studies of large databases, a number of factors have been identified that significantly affect the risk of postoperative complications. These factors can be divided into modifiable (those that can be influenced before or during treatment) and non-modifiable factors. According to the consensus of international experts (2019), the following patient-related modifiable factors are included: overweight (high body mass index), smoking, alcohol and opioid abuse, and uncorrectable comorbidities [1,11].

Non-modifiable risk factors for infectious complications include: advanced age (over 75 years), male gender, and belonging to certain ethnic groups (for example, patients of non-Caucasian race have a slightly higher incidence of infections). Knowledge of these predictors is of practical value – their identification allows for the stratification of patients by risk and the adoption of preventive measures (body weight correction, smoking cessation, compensation for concomitant diseases, etc.) before surgery, as well as the selection of the optimal intra- and postoperative management tactics [4,12].

Below are summarized key predictors and associated risks in THA:

• predictors related to the patient: advanced age, high body mass index (obesity), presence of diabetes and other systemic diseases, tobacco smoking, immune status. These factors affect the healing and resistance of the body – for example, obesity and diabetes mellitus increase the risk of infections and thrombosis, and smoking slows down tissue regeneration, increasing the likelihood of wound healing failure.

• factors of the underlying disease: in degenerative osteoarthritis, patients are usually older and often have comorbidities, while in inflammatory arthritis (rheumatoid arthritis, ankylosing spondylitis, etc.), immunosuppression is often present due to the underlying disease and therapy. For example, in patients with rheumatoid arthritis, the incidence of peri-implant infection was approximately 1.6 times higher than in patients with osteoarthritis. It is believed that this is due to the use of glucocorticoids and basic immunosuppressive drugs in rheumatic diseases, which reduces resistance to infection. Indeed, retrospective clinical data confirm that rheumatoid arthritis itself is an independent risk factor for infectious complications after endoprosthetics. Accordingly, such patients require especially careful preparation for surgery (consultation of a rheumatologist, if necessary – temporary suspension of some immunosuppressants before surgery) and more intensive monitoring in the postoperative period [4,13].

• predictors related to surgical intervention: the volume and duration of the operation, the level of blood loss, the use of a particular surgical approach, the method of implant fixation (cement-based or cementless), etc. For example, excessive duration of the operation and large intraoperative blood losses are associated with an increased risk of wound infection and hematomas, which are themselves a substrate for the development of infection. Incorrect positioning of the endoprosthesis components can lead to dislocations or uneven load on the implant, which reduces its service life. These aspects directly depend on the surgeon's skill and experience, as well as the use of modern technologies (navigation, robotic systems) that facilitate more precise prosthetic placement.

• postoperative factors: effective pain relief, early mobilization, prevention of thrombosis and infection, and adequate rehabilitation – all of these measures help reduce the risk of complications. For example, it is known that the formation of a large hematoma in the surgical wound area increases the risk of bacterial complications, so careful hemostasis and wound drainage are important components of prevention. It has also been proven that timely thromboprophylaxis significantly reduces the incidence of venous thromboembolic complications after endoprosthetics [8,14].

It is important to emphasize that the prediction of outcomes of endoprosthetics is carried out on the basis of a comprehensive analysis of all the above factors. In the world, various mathematical models and programs have been developed to help the surgeon and the patient to assess the risks and benefits of the operation. Traditionally, risk scores are used in clinical practice – for example, the scale of the American Society of Anesthesiologists (ASA), the Charlson comorbidity index, risk scales of cardiac complications and others [6,15].

These tools summarize the impact of multiple co-occurring factors into a single numerical risk score. According to recent studies, one of the strongest predictors of poor outcomes (including the risk of re-operation) is a severe overall condition according to ASA and severe obesity (body mass index > 35). It has been found that patients with ASA III-IV and morbid obesity have a significantly higher chance of developing complications compared to relatively healthy individuals.

Another important factor is the underlying cause of the surgery: for example, in patients who undergo total hip arthroplasty for a femoral neck fracture (i.e., for urgent indications), outcomes are often worse than in patients undergoing elective arthroplasty for osteoarthritis, due to the age, emergency nature of the intervention, and prodromal complications of the injury [9,16].

With the development of big data technologies and artificial intelligence, more complex models are being actively implemented, such as machine learning techniques. Major international centers have developed predictive algorithms that can accurately predict the likelihood of certain outcomes based on a variety of patient and surgical parameters. For example, a team of researchers from Harvard University has developed several machine learning models to predict the risk of re-revision (re-failure of the prosthesis) after a previous revision hip replacement. These algorithms were trained on data from 2,588 patients who underwent revision surgery, and 15.7% of them subsequently required another (repeated) revision.

As a result, we were able to achieve high prediction accuracy: four different models showed an area under the ROC curve (AUC) of over 0.80, which indicates excellent discrimination between patients with high and low risk of re-operation. Feature importance analysis revealed that the preoperative ASA status, obesity, and the reason for the first revision had the greatest impact on risk. Interestingly, the models demonstrated better benefits in terms of decision curve analysis compared to any simple strategies (operating or not operating on all patients). The authors note that such tools are suitable for optimizing patient management, for example, by allowing for the early identification of “at-risk” patients and the implementation of more aggressive preventive measures, or by choosing less invasive approaches for complex patients [17].

Similar studies are also being conducted on primary endoprosthetics. Using machine learning, it is possible to predict not only complications, but also positive outcomes – for example, the degree of improvement in function or pain on PROMs (Patient-Reported Outcome Measures). There are already studies that show the possibility of predicting how much a patient's quality of life will improve one year after total hip replacement, based on their initial data (age, pain level, comorbidities, etc.). This provides doctors with a tool to provide more accurate information to patients about the prospects of surgery and to individualize aftercare (for example, by allocating more resources for rehabilitation for those who are particularly in need based on the prognosis) [18].

Mathematical models of joint biomechanics deserve special attention. Computer modeling using the finite element method and other engineering approaches is actively used to optimize the design and positioning of implants. An example is the research conducted by the Ilmok-BMCI group (Russian Federation): over the past decades, they have used computer modeling to develop designs for hip endoprostheses and predict their biomechanical behavior. These models have helped them select the optimal materials and shapes for the implant components to reduce stress on the bone and wear on the prosthesis.

Domestic models of endoprostheses have been created and implemented, such as the cemented prosthesis "Sfen-C" and the cementless modular prosthesis "ILZA", which have shown good results in patients with osteoporosis. Mathematical modeling of the strength characteristics of the stem and acetabular component has also been used to justify their design. In general, engineering forecasting helps to predict the behavior of an implant under load at the design stage, including the distribution of pressure on the bone, the risk of micro-movements and loosening, the wear of friction pairs, and more. The data obtained in this way is used to improve the design of prostheses (for example, by adding a porous coating in areas of maximum stress, changing the angle of the endoprosthesis neck, and selecting alloys with the necessary elasticity and other properties) [19].

In addition, biomechanical models are used on an individual basis to plan surgery in complex cases. Computer simulations can be used to assess how a particular size and type of endoprosthesis will fit into a patient's anatomy and to predict the angle of installation of the components to ensure optimal range of motion without impingement. Precise preoperative planning, especially when combined with navigation and robotic installation technologies, significantly reduces the likelihood of complications such as component misalignment and endoprosthesis dislocation, which can directly impact the success of treatment.

Experience in forecasting and optimization in the CIS countries.

In post-Soviet countries, significant attention is paid to the problem of optimizing the endoprosthesis of large joints. Many scientific teams are developing areas that are in line with global trends, adapting them to local healthcare conditions. For example, one of the priorities is to reduce the frequency of postoperative complications through perioperative risk forecasting and preventive strategies. In the Russian Federation, in 2013, E.V. Reyno (Perm) defended her dissertation on forecasting and prevention of thrombotic complications after total hip arthroplasty [20].

As part of this work, a special information and analytical program was created, which automatically calculated the risk of developing hypercoagulation disorders in each patient based on objective clinical and laboratory criteria. The program took into account data on the state of the hemostasis system, medical history, the extent of the upcoming surgery, and other factors. As a result, it was possible to identify a group of patients at high risk of thromboembolic complications who required enhanced prevention measures. Moreover, based on the research, the author proposed an improved scheme for thrombosis prevention, which included prolonged pharmacotherapy for patients with signs of persistent thrombinemia. The implementation of this algorithm significantly reduced the incidence of venous thromboembolic complications after endoprosthetics [1,15].

This work illustrates an approach in which mathematical forecasting (in the form of a computer program for calculating risk) directly leads to the optimization of treatment – more targeted prevention, improved results and increased safety for patients. Another important area of research in the CIS is the prediction and prevention of purulent-septic complications (periprosthetic infections). Domestic specialists have conducted several reviews and studies summarizing the risk factors for infections after arthroplasty.

In particular, in the review by A.A. Voronov et al. (2020), the modifiable risk factors for infection in the endoprosthetic area of the TBS and knee joint were systematized and the possibilities of influencing them were discussed. Similarly to international data, factors related to the patient (obesity, smoking, diabetes, etc.), the surgical intervention (duration of surgery, intraoperative manipulations), and the postoperative management (such as timely drainage and antibiotic prophylaxis) were identified. It is emphasized that accurate identification of patients at high risk of infection allows for additional measures to be taken, such as improving wound control, postponing prosthetics until the infection is treated, and selecting preventive antibiotic therapy based on factors, etc [18,19].

In this area, work is also being carried out to create predictive models. For example, the journal "Genius of Orthopedics" published the results of the development of a method for predicting the likelihood of revision surgery on the hip joint with replacement of the acetabular component. The authors proposed an algorithm for assessing the condition of the peri-endoprosthetic bone and the fixation of the cup, which allows for predicting the risk of loosening and planning the revision procedure in a timely manner. Thus, the CIS has accumulated its own experience in predicting both general and specific complications of endoprosthetics.

The development of mathematical modeling in orthopedics in the CIS also deserves attention. Research on optimizing the design of endoprostheses using computational methods has already been mentioned.

In addition, in the 2000s, the N.N. Priorov Central Institute of Traumatology and Orthopedics (Moscow) and other centers conducted research on the stress-strain state of the bone-implant system under various prosthesis parameters. This research served as the basis for the development of competitive domestic implants that are tailored to specific pathologies (for example, special designs with larger legs and specific taper for cement fixation have been developed for patients with osteoporosis). Research has also been conducted on the use of new materials, such as titanium and its alloys, from the perspective of biocompatibility and strength, again using mathematical modeling and testing. In other words, a school has been formed in the post-Soviet space that successfully combines clinical experience in endoprosthetics with engineering analysis and scientific prediction of results [4,20].

The Successes of Uzbek Orthopedics in the Context of the Problem.

The Republic of Uzbekistan, as part of the global and post-Soviet medical community, has also achieved significant success in the field of hip joint endoprosthetics in recent decades. With an increase in the number of surgeries performed (due to both the growing needs of the population and the improvement of the traumatological and orthopedic services), Uzbek specialists are actively implementing modern approaches to optimize results. Special emphasis is placed on studying the local characteristics of patients and adapting preventive strategies [1,7].

One of the notable areas of research has been the improvement of outcomes for endoprosthetic replacement in elderly patients with proximal femoral fractures. These patients, who are typically older and often have comorbidities, are at a higher risk of complications. Uzbek traumatologists Valiev E.Yu., Valiev O.E., and their colleagues conducted a study that compared the outcomes of early hip replacement (performed within the first few days after a femoral neck injury) with delayed replacement in older patients. The sample included 227 patients aged 60–80, mostly women, who had suffered a femoral neck fracture. 110 of them formed the main group – they had total endoprosthetics performed initially, immediately after the injury, whereas 117 patients (control group) had prosthetics performed after a longer period after the injury. The results obtained convincingly demonstrated the benefits of early intervention. Immediately after surgery, 89.5% of all patients were noted favorable anatomo-functional outcomes [16].

However, in the main group, the proportion of positive outcomes was higher (93.2% versus 85% in the delayed prosthetics group), and unsatisfactory results were less common (6.8% versus 14.1%). In the long-term (long-term) period, there were no significant differences between the groups in terms of overall indicators (excellent results ~10%, good results ~32%, satisfactory results ~44% of patients), but the details showed interesting nuances. A direct relationship was found between the initial state of the bone tissue and the outcomes: patients without osteoporosis had significantly better and excellent outcomes (a total of ~40%) than those with osteopenia or osteoporosis. Moreover, an analysis of the effect of the endoprosthesis fixation method showed that the use of bone cement significantly improved the long-term outcomes in patients with osteoporosis [17].

Thus, among patients with cement fixation, ~60.9% of outcomes were considered good, while only ~39.1% of outcomes were considered good in patients with cementless fixation. This is an extremely important practical observation: it confirms that bone quality is a predictor of success, and suggests that in cases of poor bone conditions, it is preferable to use cement technology to enhance the stability of the prosthesis. This conclusion is already being implemented in the clinical practice of specialized centers in Uzbekistan.

In addition, the above-mentioned study also paid attention to the general status of patients. To objectively assess the severity of concomitant pathology, the authors used international comorbidity indices – the Charlson scale and the CIRS (Cumulative Illness Rating Scale). It was shown that with an increase in the comorbidity index, the prognosis of the results worsens: in patients with a high Charlson and CIRS score, unsatisfactory treatment outcomes were significantly more common (17.8% and 13.9%, respectively) than in patients with a low comorbid background. These data correspond to global observations and emphasize the need to optimize the overall condition of patients before surgery. Based on these findings, Uzbek specialists are increasingly involving related specialists (therapists, cardiologists, and endocrinologists) in the preoperative preparation of patients, as well as developing perioperative management protocols based on identified risk factors.

In Uzbekistan, scientific projects are also being carried out to improve the treatment methods for degenerative diseases of the hip joint before and after endoprosthetics. For example, in 2022, Akramov Vokhidjon Rustamovich defended his thesis on "Optimizing Treatment and Rehabilitation Methods for Patients with Aseptic Necrosis of the Femoral Head." Aseptic necrosis of the femoral head is a severe condition that often requires total hip replacement and requires a comprehensive approach. As part of his research, Akramov V.R. studied various factors affecting the treatment outcomes of this patient group and proposed treatment and rehabilitation schemes that can delay joint destruction and improve functional outcomes after endoprosthetics. In particular, the emphasis is on the use of predictors of the progression of necrosis (for example, according to MRI data and biochemical markers) for timely referral of the patient to surgery, as well as on personalized rehabilitation programs that take into account the degree of bone damage and concomitant diseases. Such studies fit into the general trend of the use of predictive models for decision-making: knowing the probability of rapid progression of necrosis in a particular patient, the doctor can optimize timing – plan endoprosthetics before the development of severe deformations, which will improve the result [1].

The work of V.R. Akramov and his colleagues is in line with the modern concept: "It is better to prevent complications than to treat them later."

It should be noted that Uzbekistan has a strong material and technical base for the implementation of advanced endoprosthetics technologies. The Republican Specialized Scientific and Practical Center for Traumatology and Orthopedics (RSNPMC TO) in Tashkent, as well as regional centers (Samarkand, Bukhara, etc.), are equipped with modern prostheses, instruments, and diagnostic equipment. This allows for complex surgeries using 3D navigation, intraoperative monitoring, and other techniques, which reduces the risk of technical complications [1].

But it is equally important that Uzbek specialists focus on the scientific component – the collection and analysis of data on their patients. Local registers of endoprosthetics are being formed, and scientific conferences dedicated to this topic are being held. International cooperation (for example, participation in Pirogov readings by orthopedic traumatologists, etc.) facilitates the exchange of experience. All this creates prerequisites for the further development of predictive models based on domestic clinical material. In the coming years, it is possible that national risk stratification protocols for joint endoprosthetics will be developed that take into account the specific characteristics of the Uzbek population.

3. Conclusions

Hip joint endoprosthesis optimization in degenerative and inflammatory diseases is a multifaceted task that requires the integration of clinical experience, epidemiological data, and mathematical modeling. The review showed that a huge amount of information about predictors of arthroplasty outcomes has been accumulated on a global scale. Age, concomitant diseases, features of the main pathology, technical nuances of the operation – all this affects the final result. The key trend in recent years has been preventive personalization: identifying high-risk patients and applying targeted measures to them (preoperative optimization of their condition, selection of special implants or fixation methods, and enhanced postoperative monitoring).

This approach has become possible thanks to the development of predictive models, from simple scoring systems to advanced artificial intelligence algorithms. These models allow us to predict the probabilities of certain events with high accuracy, thereby supporting clinical decisions with objective calculations.

In the CIS region and Uzbekistan, the use of predictors and mathematical forecasting has been successfully implemented. Local researchers have identified several factors that are specific to their population and have proposed specific solutions to improve outcomes. National achievements, such as the creation of thrombosis prediction programs, the analysis of infection risks, the justification of early prosthetics for femoral neck fractures, and others, fit into the overall context of global orthopedics development. It is important that such initiatives are supported at the national level and integrated into the training system and healthcare standards.

It is safe to say that further progress in endoprosthetics will be associated with an even deeper implementation of the principles of "evidence-based medicine" and mathematical modeling. The creation of multicenter registers and the integration of data using modern digital technologies will allow for the development of more advanced prediction models that are tailored to specific conditions. At the same time, the design of implants themselves will be improved, including the use of computer simulations, additive technologies (3D printing of individual prostheses), and new materials, which will expand the possibilities for customizing prostheses to meet the needs of each individual patient.

In conclusion, the optimization of hip joint endoprosthetics using predictors and mathematical forecasting methods is a prime example of the synergy between clinical medicine and technology. This approach is already yielding tangible results in terms of reducing complications and improving functional outcomes. Continued research in this area, international collaboration, and the active implementation of scientific advancements in practice will further enhance the effectiveness and safety of endoprosthetics, ensuring patients have a long and active post-operative life.